Skip to content
dev
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
ci
Oct 17, 2017
Mar 3, 2020

README.md

Status update - 2020-04-21

⚠️ NiftyNet is not actively maintained anymore. We have learned a lot in our journey and decided to redirect most of the development efforts towards MONAI.

NiftyNet

pipeline status coverage report License PyPI version

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNN) platform for research in medical image analysis and image-guided therapy. NiftyNet's modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

  • Get started with established pre-trained networks using built-in tools
  • Adapt existing networks to your imaging data
  • Quickly build new solutions to your own image analysis problems

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead.

Features

  • Easy-to-customise interfaces of network components
  • Sharing networks and pretrained models
  • Support for 2-D, 2.5-D, 3-D, 4-D inputs*
  • Efficient training with multiple-GPU support
  • Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)
  • Comprehensive evaluation metrics for medical image segmentation

NiftyNet is not intended for clinical use.

NiftyNet release notes are available here.

*2.5-D: volumetric images processed as a stack of 2D slices; 4-D: co-registered multi-modal 3D volumes

Installation

  1. Please install the appropriate TensorFlow package*:
  2. pip install niftynet

All other NiftyNet dependencies are installed automatically as part of the pip installation process.

To install from the source repository, please checkout the instructions.

Documentation

The API reference and how-to guides are available on Read the Docs.

Useful links

Citing NiftyNet

If you use NiftyNet in your work, please cite Gibson and Li, et al. 2018:

BibTeX entry:

@article{Gibson2018,
  title = "NiftyNet: a deep-learning platform for medical imaging",
  journal = "Computer Methods and Programs in Biomedicine",
  year = "2018",
  issn = "0169-2607",
  doi = "https://doi.org/10.1016/j.cmpb.2018.01.025",
  url = "https://www.sciencedirect.com/science/article/pii/S0169260717311823",
  author = "Eli Gibson and Wenqi Li and Carole Sudre and Lucas Fidon and
            Dzhoshkun I. Shakir and Guotai Wang and Zach Eaton-Rosen and
            Robert Gray and Tom Doel and Yipeng Hu and Tom Whyntie and
            Parashkev Nachev and Marc Modat and Dean C. Barratt and
            Sébastien Ourselin and M. Jorge Cardoso and Tom Vercauteren",
}

The NiftyNet platform originated in software developed for Li, et al. 2017:

Licensing and Copyright

NiftyNet is released under the Apache License, Version 2.0.

Copyright 2018 the NiftyNet Consortium.

Acknowledgements

This project is grateful for the support from the Wellcome Trust, the Engineering and Physical Sciences Research Council (EPSRC), the National Institute for Health Research (NIHR), the Department of Health (DoH), Cancer Research UK, King's College London (KCL), University College London (UCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.

You can’t perform that action at this time.